Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions (Tacotron 2)

Jonathan Shen, Ruoming Pang, Ron J. Weiss ...

Abstract

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize timedomain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of $4.53$ comparable to a MOS of $4.58$ for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the input to WaveNet instead of linguistic, duration, and $F_0$ features. We further demonstrate that using a compact acoustic intermediate representation enables significant simplification of the WaveNet architecture.

AI research summary

This paper presents Tacotron 2, a novel end-to-end neural network architecture for speech synthesis that achieves high-quality audio generation from text, significantly advancing the state of the art in text-to-speech technology. The combination of a sequence-to-sequence model with a WaveNet vocoder, along with rigorous evaluation and ablation studies, demonstrates a meaningful contribution to the field of machine learning and audio synthesis.

AI methodology analysis

The methodology presented in this paper is robust, utilizing a combination of recurrent sequence-to-sequence networks and a modified WaveNet architecture. The use of mel spectrograms as an intermediate representation simplifies the traditional TTS pipeline and allows for effective training and synthesis. The attention mechanism employed enhances the model's ability to generate coherent and contextually appropriate speech outputs. The ablation studies provide valuable insights into the importance of various components, reinforcing the design choices made by the authors.

AI results analysis

The experimental evaluation is thorough, with a clear setup for training and testing. The use of mean opinion scores (MOS) for subjective evaluation, alongside comparisons to baseline systems, effectively demonstrates the model's performance. The paper also addresses potential biases in the evaluation set and provides a detailed analysis of error types, which adds credibility to the results.

AI reproducibility analysis

The paper provides sufficient implementation details, including training configurations, model architectures, and evaluation metrics, which support reproducibility. However, the lack of a publicly available code repository limits the ease of reproduction for independent researchers.

AI limitations analysis

One notable limitation is the reliance on a single speaker's voice, which may affect the generalizability of the model to diverse speech patterns and accents. Additionally, the model's occasional mispronunciations and unnatural prosody highlight areas for further improvement.

AI broader impact analysis

The implications of this research are significant for the field of speech synthesis, as it pushes the boundaries of naturalness in TTS systems. The ability to synthesize speech that closely resembles human quality can enhance applications in virtual assistants, audiobooks, and accessibility tools. The findings may also inspire further research into end-to-end TTS systems and their integration with other modalities. This paper presents Tacotron 2, a novel end-to-end neural network architecture for speech synthesis that achieves high-quality audio generation from text, significantly advancing the state of the art in text-to-speech technology. The combination of a sequence-to-sequence model with a WaveNet vocoder, along with rigorous evaluation and ablation studies, demonstrates a meaningful contribution to the field of machine learning and audio synthesis.